Customizing a data discovery user interface based on artificial intelligence

ABSTRACT

A method includes receiving a first set of documents that correspond to data discovery documents. The method further includes generating a map of terms and words based on the first set of documents, the map of terms and words corresponding to concepts. The method further includes receiving from a first user an initial document relating to a data discovery issue. The method further includes requesting the user to provide an identification of at least one of an initial name of a first person, an initial date, and an initial keyword related to the legal issue. The method further includes generating at least one of a similar name of a second person, a similar date, and a similar keyword based on the map of terms and words. The method further includes identifying a review document based on the review document including at least one of the similar name of the second person, the similar date, and the similar keyword.

CROSS-REFERENCES TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No. 15/666,430, entitled “Customizing a Data Discovery User Interface Based on Artificial Intelligence,” filed Aug. 1, 2017, which is a continuation-in-part of U.S. patent application Ser. No. 15/432,017, entitled “Technology Platform for Data Discovery,” filed Feb. 14, 2017 (now U.S. Pat. No. 10,740,557), the contents of both of which are incorporated by reference herein.

BACKGROUND

Data discovery searches are used in a variety of applications. For example, data discovery searches are typically used in legal discovery during a lawsuit. During legal discovery, a user may need to search documents, read the documents, and then identify those documents that contain particular text that would be useful in a lawsuit. Such a discovery process can be overwhelming due to potentially thousands of documents that need to be searched, reviewed, and identified.

For example, most legal action starts with someone hearing about an issue. The issue is communicated to counsel via forwarded emails, attached documents, etc. The content may be a pleading that has been filed or it may be a rumor going around the office. The emails and attachments associated with the emails may contain a treasure-trove of information that can accelerate an assessment of a legal matter. Current data discovery platforms may not provide a way for a user to organize the information.

SUMMARY

Embodiments generally relate to a computer-implemented method to organize data discovery documents. The method includes receiving a first set of documents that correspond to data discovery documents, generating a map of terms and words based on the first set of documents, the map of terms and words corresponding to concepts, receiving from a first user an initial document relating to a data discovery issue, requesting the first user to provide an identification of at least one of an initial name of a first person, an initial date, and an initial keyword related to the legal issue, generating at least one of a similar name of a second person, a similar date, and a similar keyword based on the map of terms and words, and identifying a review document based on the review document including at least one of the similar name of the second person, the similar date, and the similar keyword.

In some embodiments, the first set of documents is a training set that is used to generate the map of terms and words based on artificial intelligence. The operations may further include providing a user interface for a search session for identifying the review document, wherein the user interface includes the at least one of the similar name of the second person, the similar date, and the similar keyword. The operations may further include receiving review times for a set of users that review the first set of documents and analyzing the review times and attributes for the first set of documents, where identifying the review document includes providing a recommendation to the first user to assign the review document to a second user from the set of users based on the analyzing. In some embodiments, the second user is a custodian with limited privileges that include an ability to tag the review document. The operations may further include providing the second user with one or more suggested tags based on at least one of the similar name, the similar date, and the similar keyword. The operations may further include providing the first user with an option to send the second set of documents to a specific review team. In some embodiments, generating the map of terms and words includes an automatic classification of law firm names.

Other aspects may include corresponding methods, systems, apparatus, and computer program products.

The specification provides a solution for the problem with organizing an overwhelming amount of information. First, the specification describes using artificial intelligence to determine the type of information that a user needs for different types of data discovery processes. Second, the specification describes a user interface that helps guide a user to understand the data discovery documents. Third, the specification uses artificial intelligence to help a custodian to manage users that perform document review by organizing the data discovery documents to reduce review time.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements.

FIG. 1 illustrates a block diagram of an example system that organizes data discovery documents according to some embodiments.

FIG. 2 illustrates a block diagram of an example computing device that organizes data discovery documents according to some embodiments.

FIG. 3 illustrates an example user interface that displays word bubbles that indicate the prevalence of different concepts based on the data discovery documents according to some embodiments according to some embodiments.

FIG. 4 illustrates an example user interface that displays a web of related concepts based on the data discovery documents according to some embodiments.

FIG. 5 illustrates an example user interface that displays definitions of related concepts based on the data discovery documents according to some embodiments.

FIG. 6 illustrates an example user interface that includes parameters for performing a search of the set of documents associated with a data discovery issue according to some embodiments.

FIG. 7 illustrates an example user interface that displays a search result summary based on a search of the data discovery documents according to some embodiments.

FIG. 8 illustrates an example user interface that displays a subset of documents from the search results that match a search term according to some embodiments.

FIG. 9 illustrates an example user interface that displays a synopsis view of a particular search result according to some embodiments.

FIG. 10 illustrates a flowchart of an example method to organize data discovery documents according to some embodiments

FIG. 11 illustrates a flowchart of another example method to organize data discovery documents according to some embodiments.

FIG. 12 illustrates a flowchart of an example method to identify a review document according to some embodiments.

DETAILED DESCRIPTION Example Overview

The specification describes a way to organize documents that relate to a data discovery issue according to some embodiments. For example, a discovery application receives a first set of documents and generates a map of terms and words based on the first set of documents. The first set of documents may be a training set that is used to generate the map of terms and words based on artificial intelligence.

The map of terms and words may be used to extract entities, locations, and organization names from a second set of documents and assign the entities, locations, and organization names a standardized set of names. For example, the second set of documents may refer to “Law Firm X” as “X, Y, Z and Associates,” “X, Y, Z, LLC,” and “Law Firm X.” The discovery application may classify the second set of documents that include any of the previous versions to all be associated with “Law Firm X.”

The discovery application may receive an initial document relating to a legal issue. For example, the initial document may be an email with a pleading in a lawsuit. The discovery application may request that the user provide an identification of an initial name of a first person, an initial date, and/or an initial keyword related to the legal issue. For example, the user may provide a name of a plaintiff in a lawsuit, a date the lawsuit was file, and the keywords “patent infringement.”

The discovery application may generate a similar name of a second person, a similar date, and/or a similar keyword based on the map of terms and words. For example, the discovery application may provide a name of a defendant in the lawsuit, a date that the patent infringement allegedly occurred, and the keywords “invalid patent.” The discovery application may also identify review documents based on the review documents including the similar name of the second person, the similar date, and/or the similar keyword. For example, one of the review documents is an answer to the complaint that includes the defendant in the lawsuit, the date that the patent infringement allegedly occurred, and the counterclaim alleging that the patent is invalid. As a result, the discovery application advantageously identifies review documents that are valuable to a person involved in the legal issue, thereby reducing the time it takes the person to find the review documents by searching through an overwhelming number of review documents.

Example System

FIG. 1 illustrates a block diagram of an example system 100 that organizes data discovery documents. The illustrated system 100 includes a discovery server 101, user devices 115 a, 115 n, a second server 120, and a network 105. Users 125 a, 125 n may be associated with respective user devices 115 a, 115 n. In some embodiments, the system 100 may include other servers or devices not shown in FIG. 1. In FIG. 1 and the remaining figures, a letter after a reference number, e.g., “115 a,” represents a reference to the element having that particular reference number. A reference number in the text without a following letter, e.g., “115,” represents a general reference to embodiments of the element bearing that reference number.

The discovery server 101 may include a processor, a memory, and network communication capabilities. In some embodiments, the discovery server 101 is a hardware server. The discovery server 101 is communicatively coupled to the network 105 via signal line 102. Signal line 102 may be a wired connection, such as Ethernet, coaxial cable, fiber-optic cable, etc., or a wireless connection, such as Wi-Fi®, Bluetooth®, or other wireless technology. In some embodiments, the discovery server 101 sends and receives data to and from one or more of the user devices 115 a, 115 n and the second server 120 via the network 105. The discovery server 101 may include a discovery application 103 a and a database 199.

The discovery application 103 a may be code and routines operable to organize data discovery documents. In some embodiments, the discovery application 103 a may be implemented using hardware including a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). In some embodiments, the discovery application 103 a may be implemented using a combination of hardware and software. The database 199 may store data discovery documents, tags, user profiles, etc.

The user device 115 may be a computing device that includes a memory and a hardware processor. For example, the user device may include a desktop computer, a mobile device, a tablet computer, a mobile telephone, a wearable device, a head-mounted display, a mobile email device, a portable game player, a portable music player, a reader device, or another electronic device capable of accessing a network 105.

In the illustrated implementation, user device 115 a is coupled to the network 105 via signal line 108 and user device 115 n is coupled to the network 105 via signal line 110. Signal lines 108 and 110 may be a wired connection, such as Ethernet, coaxial cable, fiber-optic cable, etc., or a wireless connection, such as Wi-Fi®, Bluetooth®, or other wireless technology. User devices 115 a, 115 n are accessed by users 125 a, 125 n, respectively. The user devices 115 a, 115 n in FIG. 1 are used by way of example. While FIG. 1 illustrates two user devices, 115 a and 115 n, the disclosure applies to a system architecture having one or more user devices 115.

In some embodiments, discovery application 103 b may be stored on a user device 115 a. The discovery application 103 may include a thin-client discovery application 103 b stored on the user device 115 a and a discovery application 103 a that is stored on the discovery server 101. For example, the discovery application 103 b stored on the user device 115 a may display a user interface that includes information about the data discovery documents. A user may be able to interact with the user interface to provide user input, for example, by uploading additional data discovery documents, providing the discovery application 103 b with a name of a person involved in a lawsuit, tag data discovery documents as privileged, etc. The user device 115 a may transmit the user input to the discovery application 103 a stored on the discovery server 101. The discovery application 103 a stored on the discovery server 101 may receive the user input and further organize the data discovery documents based on the user input. For example, responsive to the user uploading the additional data discovery documents, the discovery application 103 a may recategorize the data discovery documents based on the additional data discovery documents.

The second server 120 may include a processor, a memory, and network communication capabilities. The second server 120 may access the network 105 via signal line 109. The second server 120 may include an application that provides a service. For example, the second server 120 may include an electronic encyclopedia, dictionary, thesaurus, etc. The second server 120 may receive a request from the discovery application 103 for additional information from the discovery application, such as information about concepts that are similar to the term “market.” The second server 120 may provide the requested information to the discovery application.

In the illustrated implementation, the entities of the system 100 are communicatively coupled via a network 105. The network 105 may be a conventional type, wired or wireless, and may have numerous different configurations including a star configuration, token ring configuration or other configurations. Furthermore, the network 105 may include a local area network (LAN), a wide area network (WAN) (e.g., the Internet), and/or other interconnected data paths across which multiple devices may communicate. In some embodiments, the network 105 may be a peer-to-peer network. The network 105 may also be coupled to or include portions of a telecommunications network for sending data in a variety of different communication protocols. In some embodiments, the network 105 includes Bluetooth® communication networks, WiFi®, or a cellular communications network for sending and receiving data including via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, email, etc. Although FIG. 1 illustrates one network 105 coupled to the user devices 115 and the discovery server 101, in practice one or more networks 105 may be coupled to these entities.

Example Computing Device

FIG. 2 illustrates a block diagram of an example computing device 200 that organizes data discovery documents. The computing device 200 may be a discovery server 101 or a user device 115. The computing device 200 may include a processor 235, a memory 237, a communication unit 239, a display 241, and a storage device 243. Additional components may be present or some of the previous components may be omitted depending on the type of computing device 200. For example, if the computing device 200 is the discovery server 101, the computing device 200 may not include the display 241. A discovery application 103 may be stored in the memory 237. In some embodiments, the computing device 200 may include other components not listed here, such as a battery, etc. The components of the computing device 200 may be communicatively coupled by a bus 220.

The processor 235 includes an arithmetic logic unit, a microprocessor, a general purpose controller or some other processor array to perform computations and provide instructions to a display device. Processor 235 processes data and may include various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets. Although FIG. 2 includes a single processor 235, multiple processors 235 may be included. Other processors, operating systems, sensors, displays and physical configurations may be part of the computing device 200. The processor 235 is coupled to the bus 220 for communication with the other components via signal line 222.

The memory 237 stores instructions that may be executed by the processor 235 and/or data. The instructions may include code for performing the techniques described herein. The memory 237 may be a dynamic random access memory (DRAM) device, a static RAM, or some other memory device. In some embodiments, the memory 237 also includes a non-volatile memory, such as a (SRAM) device or flash memory, or similar permanent storage device and media including a hard disk drive, a compact disc read only memory (CD-ROM) device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other mass storage device for storing information on a more permanent basis. The memory 237 includes code and routines operable to execute the discovery application 103, which is described in greater detail below. The memory 237 is coupled to the bus 220 for communication with the other components via signal line 224.

The communication unit 239 transmits and receives data to and from at least one of the user device 115 and the discovery server 101 depending upon where the discovery application 103 may be stored. In some embodiments, the communication unit 239 includes a port for direct physical connection to the network 105 or to another communication channel. For example, the communication unit 239 includes a universal serial bus (USB), secure digital (SD), category 5 cable (CAT-5) or similar port for wired communication with the user device 115 or the discovery server 101, depending on where the discovery application 103 may be stored. In some embodiments, the communication unit 239 includes a wireless transceiver for exchanging data with the user device 115, discovery server 101, or other communication channels using one or more wireless communication methods, including IEEE 802.11, IEEE 802.16, Bluetooth® or another suitable wireless communication method. The communication unit 239 is coupled to the bus 220 for communication with the other components via signal line 226.

In some embodiments, the communication unit 239 includes a cellular communications transceiver for sending and receiving data over a cellular communications network including via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, e-mail or another suitable type of electronic communication. In some embodiments, the communication unit 239 includes a wired port and a wireless transceiver. The communication unit 239 also provides other conventional connections to the network 105 for distribution of files and/or media objects using standard network protocols including, but not limited to, user datagram protocol (UDP), TCP/IP, HTTP, HTTP secure (HTTPS), simple mail transfer protocol (SMTP), SPDY, quick UDP internet connections (QUIC), etc.

The display 241 may include hardware operable to display graphical data received from the discovery application 103. For example, the display 241 may render graphics to display an overlay and a resulting composite image. The display 241 is coupled to the bus 220 for communication with the other components via signal line 228.

The storage device 243 may be a non-transitory computer-readable storage medium that stores data that provides the functionality described herein. In embodiments where the computing device 200 is the discovery server 101, the storage device 243 may include the database 199 in FIG. 1. The storage device 243 may be a DRAM device, a SRAM device, flash memory or some other memory device. In some embodiments, the storage device 243 also includes a non-volatile memory or similar permanent storage device and media including a hard disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other mass storage device for storing information on a permanent basis. The storage device 243 is coupled to the bus 220 for communication with the other components via signal line 230.

The discovery application 103 may include a processing module 202, a mapping module 204, an analytics module 206, an assist module 208, and a user interface module 210.

The processing module 202 processes data discovery documents. In some embodiments, the processing module 202 includes a set of instructions executable by the processor 235 to process the data discovery documents. In some embodiments, the processing module 202 is stored in the memory 237 of the computing device 200 and can be accessible and executable by the processor 235.

The processing module 202 may receive a set of documents related to data discovery issues. For example, the data discovery issues may relate to compliance, backup data, organizational data, electronic discovery issues, etc. Electronic discovery issues are associated with discovery in a legal proceeding, such as a litigation, a government investigation, a Freedom of Information Act request, etc. The set of documents may include forwarded emails, attached documents, a filed pleading, etc. For example, the set of documents may be associated with a lawsuit that includes both a contracts cause of action and a tort cause of action. The processing module 202 may receive the set of documents from a single user, such as a project manager, or from multiple users.

The processing module 202 processes the set of documents. The processing module 202 may identify metadata associated with the set of documents. For example, the processing module 202 may identify a type of document, a date of creation of the document, dates that the document was modified, an identify of any user that modified the document, etc.

In some embodiments, the processing module 202 identifies information within the set of documents that is relevant for the data discovery process. For example, the processing module 202 may identify the following information from the set of documents: names of people, dates of events, content-rich documents, near-duplicate pivot documents, a starting point for a network of communications, privilege communication parties, and key terms. The processing module 202 may identify the names of people and a category associated with the name of people. For example, the processing module 202 may identify a type of user as a key custodian, an attorney, general counsel, a name of a law firm, etc. The processing module 202 may identify the communication parties by, for example, identifying the names of outside counsel. The processing module 202 may identify key terms in the set of documents, such as key terms that might be associated with a cause of action, a date that is significant for a particular lawsuit, an activity that could indicate guilt or liability, etc. The processing module 202 may index the set of documents based on the metadata and/or other information identified in the set of documents, such as the key terms.

In some embodiments, the processing module 202 may request the user or users to provide additional information about the set of documents. For example, the processing module 202 may instruct the user interface module 210 to provide a user interface that requests a user to input or identify a key document in the set of documents, a date associated with the set of documents, a name associated with the set of documents, a key term associated with the set of documents, a sample document, and/or a tag. The key document may include, for example, an email that triggered a lawsuit. In some embodiments, because the key document may not be part of the system, the user interface includes an option for uploading the key document and/or a text field where the user could copy and paste the text from the key document. The date may include a single date (e.g., one day, one week, one month), a range of dates (e.g., a beginning and ending date), and an identification of the significance of the date (e.g., the date is associated with a date that a contract was signed). The name may also be associated with a type of person, such as a person that is potentially privileged, a key person (e.g., in a lawsuit), a person's role in a company, a location of the person, etc. In some embodiments, the tag describes an issue (e.g., a cause of action associated with the set of documents), a folder, production, and/or work product. The analytics module 206 may use the tag provided by the user in association with a document to find other documents that are near duplicates of the tagged documents. The analytics module 206 may also use the tag as part of a training set to categorize the set of documents based on the tag. The additional information may be used by the assist module 208 to provide suggestions to the user, such as next steps for categorizing data discovery documents.

In some embodiments, the set of documents includes all documents processed by the discovery application 103. The set of documents may be related to multiple data discovery issues where a first data discovery issue is unrelated to a second data discovery issue. For example, the first data discovery issue may be a patent lawsuit and the second data discovery issue may be a breach of a covenant not to compete lawsuit. The mapping module 204 as described in greater detail below may use the set of documents to create a unified knowledge database while maintaining separation between the unrelated issues in the set of documents to maintain confidentiality.

In some embodiments, the processing module 202 may receive updated documents related to a data discovery issue. For example, the data discovery issue may be initiated by a cease and desist letter, but later a complaint and pleadings may be available. The processing module 202 processes the updated documents.

The mapping module 204 generates a map of terms and words in the set of documents that correspond to concepts. In some embodiments, the mapping module 204 includes a set of instructions executable by the processor 235 to generate the map of terms and words. In some embodiments, the mapping module 204 is stored in the memory 237 of the computing device 200 and can be accessible and executable by the processor 235.

In some embodiments, the mapping module 204 generates a map of terms and words that represents a unified knowledge database. The mapping module 204 may use the map of terms and words to automatically classify data across the set of documents. For example, the data may relate to an entity, an organization, a place, an image, etc. The entity may be a law firm and the mapping module 204 may classify all instantiations of the law firm name as being associated with the classified term. For example, for a law firm named Smith, Hugh, and Reynolds, the mapping module 204 may associate references to Smith, Hugh; Smith, Hugh, and Reynolds; SH&R, etc. to the law firm Smith, Hugh, and Reynolds. For images, the mapping module 204 may classify people and objects in the images so that one image is not classified as including a car when another image is classified as including a vehicle. Instead, the mapping module 204 applies uniformity across the set of documents.

The mapping module 204 uses the unified knowledge database to generate a map of terms and words for various concepts that applies across the set of documents. For example, the mapping module 204 categorizes relationships between concepts based on the association of terms and words in the unified knowledge database. The map may be helpful to users to identify review documents that are referring to the same concept. For example, if a user is interested in viewing review documents that include reference to the law firm Smith, Hugh, and Reynolds, it reduces review time when all the review documents are associated with Smith, Hugh, and Reynolds and the user does not have to search on all the variations of the law firm name. The map may also be used to help guide the users in identifying new search terms for organizing the electronic documents and for broadening the scope of review documents that are of interest to the user.

In some embodiments, the mapping module 204 uses the information received by the processing module 202 from the user and the map of terms and words to identify documents of interest within a set of documents associated with a data discovery issue that may be of interest to a user. The processing module 202 may receive a key document, a date, a name, a key term, a sample document, and/or a tag associated with a set of documents. The mapping module 204 may identify documents that include key dates, documents that include key names, documents that include key terms, and/or key documents within the set of documents. In some embodiments, the mapping module 204 may identify the documents of interest based on a type of issue associated with the set of documents. For example, the mapping module 204 may identify different documents of interest when the data discovery issue is a contracts case vs. a criminal investigation.

For example, the mapping module 204 may use a first set of documents that includes all data discovery documents to generate the map of terms and words. The processing module 202 may receive a second set of documents related to a data discovery issue and an identification of “market” from a user as a key term for the second set of documents. The mapping module 204 may use the map of terms and words to identify terms that are similar to market, such as business, commerce, and trade. The mapping module 204 may then identify documents of interest from the set of documents that reference market, business, commerce, or trade.

In some embodiments, the mapping module 204 determines a type of user and identifies the documents of interest based on the type of user. For example, the type of user may include a decision maker and a reviewer. A decision maker may provide inputs and iteratively train the discovery application 103 to obtain the desired results. For example, mapping module 204 may include artificial intelligence and/or machine learning that uses a training set to generate the map of terms and words and to identify documents of interest. The decision maker may provide the training set and make modifications to train the artificial intelligence and/or the machine learning to achieve the desired results. The reviewer may be a basic user that tags initial documents, is presented with similar documents that the reviewer can tag similarly, and/or that reviews documents tagged by another user for accuracy.

Once the decision maker provides modifications, the artificial intelligence and/or the machine learning of the mapping module 204 uses the modifications to improve the process for identifying subsequent documents of interest. For example, the training set may include documents associated with a first tag. The mapping module 204 may identify documents of interest with a second tag that is related to the first tag. The reviewer may determine that the mapping module 204 improperly identified the second tag and being related to the first tag. Once the reviewer provides feedback that the second tag is not related to the first tag, the mapping module 204 may remove the association between the first tag and the second tag and refrain from suggesting documents associated with the second tag in the future as being documents of interest.

The analytics module 206 analyzes a set of documents and user behavior. In some embodiments, the analytics module 206 includes a set of instructions executable by the processor 235 to analyze the set of documents and the user behavior. In some embodiments, the analytics module 206 is stored in the memory 237 of the computing device 200 and can be accessible and executable by the processor 235.

The analytics module 206 instructs the user interface module 210 to provide a user interface with a search analytics tool that includes different options for visually organizing concepts within a set of documents that are associated with a data discovery issue. In some embodiments, the analytics module 206 organizes the set of documents based on core principles, such as a type of custodian (i.e., different people who are responsible for managing the data discovery documents), file type, file extension, file content, document type, file size, or file hash (i.e., a unique identifier associated with a document). The analytics module 206 may organize the set of documents corresponding to data discovery documents based on an author or a file name. The analytics module 206 may organize a set of documents corresponding to email based on the sender, a sender domain, a receiver, a receiver domain, a subject, or an email item type. In some embodiments, the analytics module 206 automatically organizes the set of documents based on different features associated with the set of documents, such as topics, topic terms, location, organization, a person, a summary, or a language.

The analytics module 206 may instruct the user interface module 210 to generate a user interface that displays a prevalence of different words and terms in different formats. For example, the user interface module 210 may generate a user interface that includes a word cloud that categorizes terms and words in a set of documents based on a prevalence of each of the terms and the words in the set of documents. The analytics module 206 may instruct the user interface module 210 to include an option to search for a subset of the set of documents that include a word from the word cloud. The analytics module 206 may instruct the user interface module 210 to include an option to search for a word that is semantically similar to another word in the word cloud.

In another example, the user interface module 210 may generate a user interface that includes word bubbles that display the terms and words based on a prevalence of the terms and words in a set of documents. Other examples may include a user interface that includes words with semantic similarity to a selected term, a user interface that organizes metadata associated with the set of documents, a user interface that organizes work product associated with the set of documents, a user interface that displays clustered topics associated with the set of documents, etc.

In some embodiments, the user interface module 210 may provide a user with the option of generating the word cloud or the word bubble for a subset of the set of documents, such as a subset of documents that were created during a particular date range, a subset of documents that also include another word or that are associated with a particular tag, a subset of documents that were created by a particular user or came from email addresses that are associated with a particular domain name, a subset of documents that are associated with a particular type (e.g., location, organization, person, category, social security number, keyword language), etc. In some embodiments, clicking on a word or term within the user interface causes the analytics module 206 to retrieve a subset of documents within the set of documents that include the selected word, term, or type.

Turning to FIG. 3, an example user interface 300 is illustrated that displays word bubbles that indicate the prevalence of different concepts based on data discovery documents. The user interface 300 provides a first panel 305 for organizing the set of documents, a second panel 310 that details a top number of results for a selected category, and a third panel 315 that includes a visual categorization of the results for the selected category. A user select any subsection under a category to view data discovery documents associated with the selected category. For example, where the category is “core,” a user may view data discovery documents based on custodian, file type, file extension, file content, type, file size, and file hash. Where the category is “electronic document,” a user may view data discovery documents based on author or file name. Where the category is “email,” the user may view data discovery documents based on sender, sender domain, receiver, receiver domain, subject, or email item type. Where the category is “automatically identified,” the user may view data discovery documents based on topics, topic terms, location, organization, person, summary, or language. Where the category is “date,” the user may view data discovery documents based on a date.

In this example, a user requested a word bubble that illustrates emails from a set of documents that are organized based on a sender. The emails are associated with a top nine senders. The senders are represented with word bubbles that are sized based on the prevalence of emails from those particular senders. In this example, although the bulk of the emails are from an unknown sender, the unknown sender is not part of the word bubbles since “unknown” is an unhelpful category.

Clicking on one of the boxes to the left of the results in the second panel 310 causes the analytics module 206 to retrieve the corresponding results. For example, clicking on the box to the left of “rapp” causes the analytics module 206 to instruct the user interface module 210 to display the 55 emails from the set of documents that were sent by “rapp.”

In some embodiments, the analytics module 206 uses the map of terms and words to organize data for the user and provide options for other search terms to use to organize the set of documents. For example, where the word cloud or the word bubble displays key terms, selection of one of the key terms may cause the analytics module 206 to instruct the user interface module 210 to display related concepts, information about related concepts, synonyms of a selected word, etc.

In some embodiments, responsive to a user selecting a word from the word cloud or a word bubble, the analytics module 206 instructs the user interface module 210 to provide a user with a visual representation of different meanings for a selected word, connections between the different meanings, and one or more synonyms that correspond to each of the different meanings.

FIG. 4 illustrates an example user interface 400 that displays a web of related concepts based on the data discovery documents. In this example, a user interface module 210 displays a user interface 400 with an analyzed terms panel 405 that includes the top key terms. The “market” term is selected and the analytics module 206 instructs the user interface module 210 to display concepts that are related to “market.” For the circle tab 410 the analytics module 206 organizes the related concepts according to senses. The senses are further divided according to related concepts. The user interface 400 further includes a search panel 415 where a user may specify search terms for searching the set of documents.

In another embodiment, responsive to a user selecting a word from the word cloud or a word bubble, the analytics module 206 instructs the user interface module 210 to provide a user with a tree view of the different meanings for the first word and one or more synonyms that correspond to teach of the different meanings.

FIG. 5 illustrates an example user interface 500 that displays definitions of related concepts based on the data discovery documents. In this example, the senses that were identified in FIG. 4 for the circle tab are defined in the tree tab 505. Under at least some of the senses are synonyms of the selected concept. For example, sense 1 is defined as the world of commercial activity where goods and services are bought and sold and the synonyms of market that correspond to sense 1 are activity, business, and monopoly.

The analytics module 206 analyzes user behavior. In some embodiments, the analytics module 206 uses artificial intelligence to analyze the performance of users. The processing module 202 may receive a set of documents and the analytics module 206 may use the set of documents as a training set. The analytics module 206 may identify user actions corresponding to the set of documents. For example, the analytics module 206 may identify user identifiers corresponding to different users, how long the users spend reviewing discovery documents in the set of documents, tags that the users associate with discovery documents, etc.

The analytics module 206 may analyze the set of documents, automatically classify the set of documents, and compare the tags provided by users to the automatic classifications to determine the accuracy of the tags. The analytics module 206 may also determine the accuracy and a time it takes users to review the discovery documents. In some embodiments, the analytics module 206 may identify which users are most accurate based on the comparison of the tags to the automatic classifications. In some embodiments, the analytics module 206 generates a review score based on an accuracy of the tags and a time it takes to review the discovery documents.

The analytics module 206 may recommend different users to review different types of documents based on the accuracy of the tags and/or the time it takes to review the discovery documents. For example, the analytics module 206 may recommend that a first user be assigned to a set of review documents that correspond to a data discovery issue involving contracts because the first user has a high accuracy of classifying data discovery documents involving contracts and the first user efficiently reviews data discovery documents involving contracts. In some embodiments, the analytics module 206 may recommend users based on the review score.

In some embodiments, users may be organized according to different review teams. For example, a group of users may be assigned to review a set of documents. The analytics module 206 may instruct the user interface module 210 to provide an option for a user, such as an administrator, to assign a particular review team to review a set of documents. The analytics module 206 may recommend different types of review teams based on a data discovery issue associated with the set of documents. For example, the analytics module 206 may assign review scores to different review teams depending on the performance of users in the review teams and the analytics module 206 may recommend different review teams based on the review scores.

The assist module 208 performs searches of the data discovery documents. In some embodiments, the assist module 208 includes a set of instructions executable by the processor 235 to perform searches. In some embodiments, the assist module 208 is stored in the memory 237 of the computing device 200 and can be accessible and executable by the processor 235. In some embodiments, the actions performed by the assist module 208 are actions associated with a service called “CaseAssist.” For example, the user interfaces illustrated in FIGS. 6-9 are based on actions performed by the assist module 208 that are provided to a user as part of a CaseAssist service.

The assist module 208 receives information from the processing module 202 and/or the memory 237 about a set of documents associated with a legal issue. For example, the set of documents are associated with a particular legal investigation. The assist module 208 instructs the user interface module 210 to provide a user interface for the user to provide parameters for searching the set of documents. The parameters include a session name, a role of users associated with the set of documents (e.g., case administrator, client administrator, client reviewer, etc.), people associated with the set of documents (e.g., people that are potential parties to a lawsuit), dates associated with the set of documents, keywords, a similar document search, and work product (e.g., tags associated with the set of documents). The assist module 208 uses the information identified by the processing module 202 as being associated with the data discovery issue to make suggestions for search terms for the parameters. For example, the processing module 202 identifies people, key terms, and dates that are associated with the data discovery issue. The processing module 202 instructs the user interface module 210 to provide the identified people, key terms, and dates as suggested parameters for a search, for example, by providing a drop-down box with the people, key terms, and dates; by providing a suggestion that corresponds to a person, a key term, or a date responsive to a user entering a letter in a text field; etc.

Turning to FIG. 6, an example user interface 600 is illustrated that includes parameters for performing a search of the set of documents associated with a data discovery issue. For example, FIG. 6 illustrates a user interface 600 for creating a CaseAssist session for searching the set of documents.

The user interface 600 includes an option for specifying that the search is a new session or an existing session. If the session is an existing session, the assist module 208 stores the existing session in association with a session name. If a user makes changes to an existing session, the assist module 208 may save the changes for the existing session. In this example, the session is an existing session. A user may identify a list of existing sessions by selecting the drop-down box 605 for existing session names.

The user interface 600 includes an option for selecting the role of people associated with the data discovery issue. Because different types of people may have access to different subsets of documents within the set of documents, a user may want to search subsets of documents associated with the different types of people. In this example, a user specified using the drop-down box 610 that the search should include the subsets of documents associated with the case administrator, the client administrator, and the client reviewer.

The user interface 600 includes an option for requesting documents that are associated with particular people. In this example, the user interface includes a text field 615 where a user may specify the name of a person and add the person by selecting the add icon 617. The assist module 208 may suggest people to add to the search. For example, continuing with the example described in FIG. 3 where the top sender for the emails is Pete Davis, the user interface module 210 may display an option to add Pete Davis as a person to be searched. Alternatively, if the user adds the letter “P” within the text field 615, the user interface module 210 may populate the rest of the text field 615 with “Pete Davis” as a suggestion.

Once a user selects the add icon 617 to add the person to the search, the user interface module 210 creates an icon for the added person. For example, responsive to the user adding “Pete Davis” to the search, the user interface module 210 adds a Pete Davis icon 620 to the user interface 600. The user may remove a person from the search by selecting the “X” within the person icon. For example, the user may remove “Pete Davis” from the search by selecting the X within the Pete Davis icon 620.

The user interface 600 includes an option for requesting documents that are associated with particular dates. In this example, the user interface includes a text field 625 where a user may specify a date by adding the date in the format yyyy/mm/dd. The assist module 208 may suggest dates to add to the search. For example, the processing module 202 identified “2001/09/30” as an important date for the data discovery issue.

Once a user selects the add icon 627 to add a date to the search, the user interface module 210 creates an icon for the added date. For example, responsive to the user adding “2001/09/30” to the search, the user interface module 210 adds a 2001/09/30 icon 630 to the user interface 600. The user may remove a date from the search by selecting the “X” within the date icon. For example, the user may remove “2001/09/30” from the search by selecting the X within the 2001/09/30 icon 630.

The user interface 600 includes an option for requesting documents that are associated with keywords. In this example, the user interface includes a text field 635 where a user may specify a keyword and add the keyword by selecting the add icon 637. The assist module 208 may suggest keywords to add to the search. For example, the assist module 208 may suggest keywords based on the key terms identified by the processing module 202 as being important to the data discovery issue.

Once a user selects the add icon 637 to add a keyword to the search, the user interface module 210 creates an icon for the added keyword. For example, responsive to the user adding “never” as a keyword, the user interface module 210 adds a never icon 640 to the user interface 600. The user may remove a keyword from the search by selecting the “X” within the keyword icon. For example, the user may remove “never” from the search by selecting the X within the never icon 640.

The user interface 600 includes an option for specifying that similar documents should be searched. For example, if a user selects the option, the assist module 208 identifies documents from the set of documents that are similar matches based on the search terms. For example, the similar documents may match 90% of the search terms. In some embodiments, the user interface 600 may include an option for specifying a subset of documents to be searched for identifying similar documents within the subset.

The user interface 600 includes an option for specifying work product tags. In this example, the IPAD tag 650 is for a user device associated with a user that provided document review of the set of documents associated with the data discovery issue.

FIG. 7 illustrates an example user interface 700 that displays a search result summary based on a search of the data discovery documents. In some embodiments, responsive to a user requesting a search of the set of documents based on the parameters specified in FIG. 6, the assist module 208 generates a summary of the search results. The search result summary may be a CaseAssist search results summary generated by the assist module 208. In this example, the assist module 208 identified 15201 documents that match the search parameters, 55 gigabytes of data represented by the 15201 documents, a last modification date of Sep. 1, 2005, and 1002 documents that are similar to the search parameters.

FIG. 8 illustrates an example user interface 800 that displays a subset of documents from the search results that match a search term. The search results may be CaseAssist search results generated by the assist module 208. In this example, a user enters “Money” into the text field 805 and the assist module 208 retrieves search results from a set of documents associated with a data discovery issue that include the word “money.” The assist module 208 instructs the user interface module 210 to generate a user interface 800 with a first panel 810 that categorizes the set of documents based on a type of document. In this example, the user interface 800 includes the emails associated with different senders that include emails with the word “money.” To the right of each type of sender is a number of search results that correspond to the word “money.”

The user interface 800 includes a second panel 815 that includes a preview of the matching documents. Each search result is illustrated with an image of the search result, a unique identifier for the search result, a type of search result, sample text from the search result with the matching search term, and a date that the search result was created. For example, the first search result includes a preview 820 of the first page of the PowerPoint, the unique identifier for the search result is REV0000043, the sample text from the search result includes the word “money”, and the search result was created on Feb. 4, 2002. In some embodiments, the user interface module 210 generates a popup when a user's pointing device hovers over one of the search results. The popup may include relevant topics, classes of documents, similar documents, etc.

FIG. 9 illustrates an example user interface 900 that displays a synopsis view of a particular search result. The search result may be CaseAssist quick review generated by the assist module 208. The user interface module 210 generates a user interface 900 with a quick view for a data discovery document. For example, the user interface module 210 generates the quick view responsive to a user selecting the search result from the search results illustrated in FIG. 8. The quick view includes a name of the search result, a link to the search result, a date (e.g., a date of creation, a date of the last modification, etc.), a list of categories generated by the processing module 202 (e.g., finance, business, etc.), a document summary generated by the assist module 208, case tags that were provided by people that reviewed the document or that were automatically generated by the processing module 202, and assist tags that were provided by people that reviewed the document or that were automatically generated by the processing module 202.

In some embodiments, the assist module 208 analyzes user actions and provides additional information related to the user actions. In a first example, the assist module 208 identifies a threshold number of times that a user searches for documents that correspond to a particular search term. If the user inputs the search term more than the threshold number of times, the assist module 208 may identify documents that are similar to the matching results, such as documents that are associated with a related search term, are 90% similar to the matching documents, etc. In a second example, the assist module 208 may track an amount of time that a user spends reviewing documents in a set of documents associated with a data discovery issue. The assist module 208 may determine an average amount of time that the user spends reviewing documents. If the user exceeds the review time for a document by a threshold amount of time, the assist module may identify similar documents and provide them to the user. In a third example, the assist module 208 may instruct the user interface module 210 to generate a user interface with an option to take documents with an assist tag. If the user tags a document with the assist tag, the assist module 208 may identify documents that are similar to the tagged document and provide the similar documents to the user.

In some embodiments, the assist module 208 helps simplify the organization of data discovery documents by asking a user about some key information and providing review documents based on the key information. First, the user may provide the assist module 208 with an initial document relating to a data discovery issue. For example, the initial document may be an email with a pleading in a lawsuit. The assist module 208 may instruct the user interface module 210 to request the user to provide an identification of at least one of an initial name of a first person, an initial date, a location, and/or an initial keyword related to the legal issue. Based on the information received from the user, the assist module 208 may generate a similar name of a second person, a similar date, and/or a keyword. The assist module 208 may use a map of terms and words generated by the mapping module 204 to generate the similar name of the second person, the similar date, and/or the keyword.

The assist module 208 may identify one or more review documents based on the one or more review documents including the similar name of the second person, the similar date, and/or the keyword. For example, the assist module 208 may use the email with the pleading and the information provided by the user to identify other documents from the lawsuit that the user may want to review. In another example, the assist module 208 may receive an initial document where the user identifies “market” as a keyword. The assist module 208 identify review documents from a set of documents that include market and the similar keywords merchandise, offer, trade, deal, sell, shop, activity, business, monopoly, marketplace, shelf, industry, class, alter, change, commerce, and modify. This advantageously simplifies the process for the user by providing the user with the next steps in data discovery management instead of the user having to find the relevant documents.

The assist module 208 may identify the review document for the user that provided the initial document. Alternatively, the assist module 208 may identify a set of review documents and provide a different user with the set of review documents. For example, the different user may be a custodian with limited privileges that include an ability to tag the set of review documents. In some embodiments, the assist module 208 provides the different user with one or more suggested tags based on the similar name, the similar date, and/or the similar keyword. In some embodiments, the assist module 208 may instruct the user interface module 210 to generate a user interface for a search session for identifying the review document. The search session may include the similar name of the second person, the similar date, and/or the keyword.

In some embodiments, the assist module 208 determines what information to provide to a user based on artificial intelligence and/or machine learning that takes the user's past actions into consideration. For example, if a user has worked on 25 cases and the user consistently uploads an initial document and then performs a search for related documents based on similar people, the assist module 208 may provide a CaseAssist search session as the next step after the user uploads the initial document.

The assist module 208 may instruct the user interface module 210 to generate an email or other type of notification for the user to provide the user with the documents of interest and a number of documents associated with different parameters. For example, the user interface module 201 may generate an email for the user that states “We have identified some documents that might be of interest to you. Documents that correlate to: key dates (47 documents); key names (300 documents); key terms (257 documents); key documents (89 documents).”

In some embodiments, the assist module 208 instructs the user interface module 210 to provide a user with an option for sending review documents to other users. For example, a set of documents may be organized according to whether any of the review documents in the set of documents have already been reviewed. The user interface may include an option for only sending the review documents in the set of documents that have not yet been reviewed to a particular user for review. In some embodiments, the user interface includes an option for selecting a subset of documents from multiple sets of documents to a reviewer for quality control. In this example, the subset of documents will already have been reviewed by one or more other users.

The user interface module 210 generates user interfaces. In some embodiments, the user interface module 210 includes a set of instructions executable by the processor 235 to generate the user interface. In some embodiments, the user interface module 210 is stored in the memory 237 of the computing device 200 and can be accessible and executable by the processor 235.

In some embodiments, the user interface module 210 receives instructions from the processing module 202, the mapping module 204, the analytics module 206, and/or the assist module 208 to generate a user interface. The user interface module 210 generates the user interface and presents it to a user, for example, by instructions a web browser to display the user interface on a user device 115.

In some embodiments, the user interface module 210 generates a different user interface depending on the type of user. For example, the user may be a custodian that reviews documents, an administrator that assigns data discovery documents to custodians to review, etc. In some embodiments, a user may have multiple roles. For example, the user interface module 210 may provide an option for an administrator to also work as a custodian to review data discovery documents.

Example Methods

FIG. 10 illustrates a flowchart of an example method 1000 to organize data discovery documents. The method 1000 is performed by a discovery application 103 stored on a computing device 200, such as a user device 115, a discovery server 101, or in part a user device 115 and in part a discovery server 101.

At block 1002, a set of documents related to data discovery issues is received, where at least a first data discovery issue is unrelated to a second data discovery issue. For example, the set of documents includes all data discovery documents processed by the discovery application 103. A first set of documents may be associated with the first data discovery issue and a second set of documents may be associated with the second data discovery issue.

At block 1004, a map of terms and words for the set of documents is generated, where the map of terms and words correspond to concepts. At block 1006, a user interface that includes a search analytics tool is provided to a user associated with the first data discovery issue. The user may be, for example, a custodian that tags different data discovery documents that are part of the first set of documents. The search analytics tool may provide information based on the map of terms and words.

FIG. 11 illustrates a flowchart of another example method 1100 to organize data discovery documents. The method 1100 is performed by a discovery application 103 stored on a computing device 200, such as a user device 115, a discovery server 101, or in part a user device 115 and in part a discovery server 101.

At block 1102, a first set of documents related to data discovery issues is received, where the first set of documents is associated with a first data discovery issue. For example, the first data discovery issue may be a civil litigation involving a torts cause of action and a breach of contract cause of action. At block 1104, a map of terms and words for the first set of documents that correspond to concepts is generated.

At block 1106, a second set of documents that is associated with a second data discovery issue is received from a user, where the first data discovery issue is unrelated to the second data discovery issue. At block 1108, it is requested that the user provide at least one of a key document, a date, a name, a key term, a sample document, and a tag, where the tag includes at least one of an issue, a folder, a production, and a work product.

At block 1110, the second set of documents is classified based on the map of terms and words and the at least one of the key document the date, the name, the key term, the sample document, and the tag. At block 1112, the user is provided with documents of interest based on classifying the second set of documents and the at least one of the key document, the date, the name, the key term, the sample document, and the tag.

FIG. 12 illustrates a flowchart of an example method 1200 to identify a review document. The method 1200 is performed by a discovery application 103 stored on a computing device 200, such as a user device 115, a discovery server 101, or in part a user device 115 and in part a discovery server 101.

At block 1202, a first set of documents that corresponds to data discovery documents is received. For example, the first set of documents may be a training set that is used to generate the map of terms and words based on artificial intelligence. At block 1204, a map of terms and words is generated based on the first set of documents, where the map of terms and words correspond to concepts. For example, the map of terms and words corresponds to a unified knowledge database that automatically classifies data across the set of documents.

At block 1206, an initial document relating to a legal issue is received from a user. At block 1208, a request is made to the user to provide an identification of at least one of an initial name of a first person, an initial date, and an initial keyword related to the legal issue. For example, a user interface may include fields where the user may input the requested information.

At block 1210, at least one of a similar name of a second person, a similar date, and a similar keyword are generated based on the map of terms and words. At block 1212, a review document is identified based on the review document including at least one of the similar name of the second person, the similar date, and the similar keyword.

In the above description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the specification. It will be apparent, however, to one skilled in the art that the disclosure can be practiced without these specific details. In some instances, structures and devices are shown in block diagram form in order to avoid obscuring the description. For example, the embodiments can be described above primarily with reference to user interfaces and particular hardware. However, the embodiments can apply to any type of computing device that can receive data and commands, and any peripheral devices providing services.

Reference in the specification to “some embodiments” or “some instances” means that a particular feature, structure, or characteristic described in connection with the embodiments or instances can be included in at least one implementation of the description. The appearances of the phrase “in some embodiments” in various places in the specification are not necessarily all referring to the same embodiments.

Some portions of the detailed descriptions above are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic data capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these data as bits, values, elements, symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms including “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission, or display devices.

The embodiments of the specification can also relate to a processor for performing one or more steps of the methods described above. The processor may be a special-purpose processor selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory computer-readable storage medium, including, but not limited to, any type of disk including optical disks, ROMs, CD-ROMs, magnetic disks, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memories including USB keys with non-volatile memory, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.

The specification can take the form of some entirely hardware embodiments, some entirely software embodiments or some embodiments containing both hardware and software elements. In some embodiments, the specification is implemented in software, which includes, but is not limited to, firmware, resident software, microcode, etc.

Furthermore, the description can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer-readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

A data processing system suitable for storing or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. 

What is claimed is:
 1. A method to automatically identify and provide a document for review for data discovery, the method comprising: receiving a first set of documents that correspond to data discovery documents; generating a map of terms and words based on the first set of documents, the map of terms and words including a plurality of standardized names, wherein generating the map of terms and words includes assigning a respective standardized name to each of a plurality of words in the first set of documents that correspond to a same concept; assigning, for review, a subset of the first set of documents that include the respective standardized name; receiving review times for a set of users that review one or more documents in the subset of the first set of documents; analyzing the review times and attributes for the subset of the first set of documents; identifying a review document based on the analyzing, wherein the review document is not part of the first set of documents; providing, without further user input, a user interface that includes a recommendation to a first user to assign the review document to a second user from the set of users; and assigning, for review, the review document to the second user.
 2. The method of claim 1, further comprising: receiving an assist tag associated with the review document from the second user; and responsive to receiving the assist tag, identifying similar documents that are similar to the review document for the second user to review.
 3. The method of claim 1, further comprising: determining that one or more documents in the first set of documents is associated with a first tag; associating the first tag with a second tag; receiving feedback from the second user that the second tag is not related to the first tag; and removing the association between the first tag and the second tag.
 4. The method of claim 1, further comprising: generating automatic tags to the first set of documents; and determining an accuracy of a review of the set of users based on a comparison of reviewer tags applied by the set of users to the first set of documents to the automatic tags.
 5. The method of claim 4, wherein providing the recommendation to assign the review document to the second user is further based on the accuracy of the review of the second user.
 6. The method of claim 1, further comprising: determining, with machine learning, past actions of the second user performed when reviewing documents; and customizing the user interface for the second user based on the past actions.
 7. The method of claim 6, wherein the first set of documents are training data.
 8. A system to automatically identify and provide a document for review, the system comprising: one or more processors coupled to a memory; an image processing module stored in the memory and executable by the one or more processors, the image processing module operable to receive a first set of documents that correspond to data discovery documents; a mapping module stored in the memory and executable by the one or more processors, the mapping module operable to generate a map of terms and words based on the first set of documents, the map of terms and words including a plurality of standardized names, wherein generating the map of terms and words includes assigning a respective standardized name to each of a plurality of words in the first set of documents that correspond to a same concept; an assist module stored in the memory and executable by the one or more processors, the assist module operable to assign for review, a subset of the first set of documents that include the respective standardized name, receive review times for a set of users that review one or more documents in the subset of the first set of documents, analyze the review times and attributes for the subset of the first set of documents, and identify a review document based on the analyzing, wherein the review document is not part of the first set of documents; and a user interface module stored in the memory and executable by the one or more processors, the assist module operable to provide, without further user input, a user interface that includes a recommendation to a first user to assign the review document to a second user from the set of users; wherein the assist module, is further operable to assign, for review, the review document to the second user.
 9. The system of claim 8, wherein the assist module is further operable to: receive an assist tag associated with the review document from the second user; and responsive to receiving the assist tag, identify similar documents that are similar to the review document for the second user to review.
 10. The system of claim 8, wherein the assist module is further operable to: determine that one or more documents in the first set of documents is associated with a first tag; associate the first tag with a second tag; receive feedback from the second user that the second tag is not related to the first tag; and remove the association between the first tag and the second tag.
 11. The system of claim 8, wherein the assist module is further operable to: generate automatic tags to the first set of documents; and determine an accuracy of a review of the set of users based on a comparison of reviewer tags applied by the set of users to the first set of documents to the automatic tags.
 12. The system of claim 11, wherein providing the recommendation to assign the review document to the second user is further based on the accuracy of the review of the second user.
 13. The system of claim 8, wherein the mapping module is further operable to determine, with machine learning, past actions of the second user performed when reviewing documents and wherein the user interface module is further operable to customize the user interface for the second user based on the past actions.
 14. The system of claim 13, wherein the first set of documents are training data.
 15. A non-transitory computer-readable storage medium to automatically identify and provide a document for review, the computer-readable storage medium carrying program instructions thereon, the program instructions when executed by one or more processors cause the one or more processors to perform operations comprising: receiving a first set of documents that correspond to data discovery documents; generating a map of terms and words based on the first set of documents, the map of terms and words including a plurality of standardized names, wherein generating the map of terms and words includes assigning a respective standardized name to each of a plurality of words in the first set of documents that correspond to a same concept; assigning, for review, a subset of the first set of documents that include the respective standardized name; receiving review times for a set of users that review one or more documents in the subset of the first set of documents; analyzing the review times and attributes for the subset of the first set of documents; identifying a review document based on the analyzing, wherein the review document is not part of the first set of documents; providing, without further user input, a user interface that includes a recommendation to a first user to assign the review document to a second user from the set of users; and assigning, for review, the review document to the second user.
 16. The computer-readable storage medium of claim 15, wherein the operations further comprise: receiving an assist tag associated with the review document from the second user; and responsive to receiving the assist tag, identifying similar documents that are similar to the review document for the second user to review.
 17. The computer-readable storage medium of claim 15, wherein the operations further comprise: determining that one or more documents in the first set of documents is associated with a first tag; associating the first tag with a second tag; receiving feedback from the second user that the second tag is not related to the first tag; and removing the association between the first tag and the second tag.
 18. The computer-readable storage medium of claim 15, wherein the operations further comprise: generating automatic tags to the first set of documents; and determining an accuracy of a review of the set of users based on a comparison of reviewer tags applied by the set of users to the first set of documents to the automatic tags.
 19. The computer-readable storage medium of claim 18, wherein providing the recommendation to assign the review document to the second user is further based on the accuracy of the review of the second user.
 20. The computer-readable storage medium of claim 15, wherein the operations further comprise: determining, with machine learning, past actions of the second user performed when reviewing documents; and customizing the user interface for the second user based on the past actions. 